• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1037-1040
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• 1988

In this study, we performed the recognition test on Korean vowel using peripheral auditory model. In addition, for the purpose of objective comparision, the recognition test is performed by extracting LPC cepstrum coefficients from the same data. And the same speech data are mixed with the Guaussian white noise quantitatively, then we repeated the same test, too. So we verified that this auditory model has a adaptability on noise.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.620-623
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• 1988

A vowel is based on the recognition of a phoneme. Thus it is necessary for the programming of an algorithm to achieve the speech recognition in that case. In this paper, cepstrum is used for a voiced-unvoiced decision and speech parameters are extracted by linear prediction coding. Using these parameters, a speech understanding algorithm has been developed.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.10-18
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• 2010

Due to a large number of classes in Hangul character recognition, it is usual to use the six-type preclassification stage. After the preclassification, the first consonent, vowel, and last consonent can be classified separately. Though each of three components has a few of classes, classification errors occurs often due to shape similarity such as 'ㅔ' and 'ㅖ'. So this paper proposes a hierarchical recognition method which adopts multi-stage tree structures for each of 6-types. In addition, to reduce the interference among three components, the method uses the recognition results of first consonents and vowel as features of vowel classifier. The recognition accuracy for the test set of PHD08 database was 98.96%.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.163-174
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• 2014

Korean L2 speakers have many problems learning the pronunciation of English words. One of these problems is vowel epenthesis. Vowel epenthesis is the insertion of vowels into or between words, and Korean learners of English typically do this between successive consonants, either within clusters, or across syllables, word boundaries or following final coda consonants. The aim of this study was to investigate whether individual differences in vowel epenthesis are more closely related to the perception and production of segments (vowels and consonants) and prosody or if they are relatively independent from these processes. Subjects completed a battery of production and perception tasks. They read sentences, identified vowels and consonants, read target words likely to have epenthetic vowels (e.g., abduction) and demonstrated stress recognition and epenthetic vowel perception. The results revealed that Korean second-language learners (L2) have problems with vowel epenthesis in production and perception, but production and perception abilities were not correlated with one another. Vowel epenthesis was strongly related to vowel production and perception, suggesting that problems with segments may be combined with L1 phonotactics to produce epenthesis.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.583-590
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• 2016

Recently, the rapidly increasing interest in IoT in almost all areas of casual human life has led to wide acceptance of speech recognition as a means of HCI. Simultaneously, the demand for speech recognition systems for mobile environments is increasing rapidly. The server-based speech recognition systems are typically fast and show high recognition rates; however, an internet connection is necessary, and complicated server computation is required since a voice is recognized by units of words that are stored in server databases. In this paper, we present a novel method for recognizing the Korean vowel 'ㅗ', as a part of a phoneme based Korean speech recognition system. The proposed method involves analyses of waveform patterns in the time domain instead of the frequency domain, with consequent reduction in computational cost. Elementary algorithms for detecting typical waveform patterns of 'ㅗ' are presented and combined to make final decisions. The experimental results show that the proposed method can achieve 89.9% recognition accuracy.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.782-791
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• 1991

In the fields of pattern recognition such as speech recognition, several new techniques using Artifical Neural network Models have been proposed and implemented. In particular, the Multilayer Perception Model has been shown to be effective in static speech pattern recognition. But speech has dynamic or temporal characteristics and the most important point in implementing speech recognition systems using Artificial Neural Network Models for continuous speech is the learning of dynamic characteristics and the distributed cues and contextual effects that result from temporal characteristics. But Recurrent Multilayer Perceptron Model is known to be able to learn sequence of pattern. In this paper, the results of applying the Recurrent Model which has possibilities of learning tedmporal characteristics of speech to phoneme recognition is presented. The test data consist of 144 Vowel+ Consonant + Vowel speech chains made up of 4 Korean monothongs and 9 Korean plosive consonants. The input parameters of Artificial Neural Network model used are the FFT coefficients, residual error and zero crossing rates. The Baseline model showed a recognition rate of 91% for volwels and 71% for plosive consonants of one male speaker. We obtained better recognition rates from various other experiments compared to the existing multilayer perceptron model, thus showed the recurrent model to be better suited to speech recognition. And the possibility of using Recurrent Models for speech recognition was experimented by changing the configuration of this baseline model.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.46-49
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• 2003

The use of confidence measures for word/utterance verification has become art essential component of any speech input application. Confidence measures have applications to a number of problems such as rejection of incorrect hypotheses, speaker adaptation, or adaptive modification of the hypothesis score during search in continuous speech recognition. In this paper, we present a new utterance verification method using vowel string. Using subword HMMs of VCCV unit, we create anti-models which include vowel string in hypothesis words. The experiment results show that the utterance verification rate of the proposed method is about 79.5%.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.150-159
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• 1998

This paper described the recognition of the Off-line handwritten Hangul based on neural network using a feature extraction method. Features of Hangul can be extracted by a $5{\times}5$ window method which is the modified $3{\times}3$ mask method. These features are coded to binary patterns in order to use neural network's inputs efficiently. Hangul character is recognized by the consonant, the vertical vowel, and the horizontal vowel, separately. In order to verify the recognition rate, three different coding methods were used for neural networks. Three methods were the fixed-code method, the learned-code I method, and the learned-code II method. The result was shown that the learned-code II method was the best among three methods. The result of the learned-code II method was shown 100% recognition rate for the vertical vowel, 100% for the horizontal vowel, and 98.33% for the learned consonants and 93.75% for the new consonants.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.247-252
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• 2017

The purpose of this study is to suggest the visual teaching method for the English vowel pronunciation, especially for the hearing-impaired who mostly rely on the visual aids, based on the SVM technique. By extracting phonetic features using the SVM technique from the sounds that are hard to hear by ear, the lip shapes for each vowel were refined. The lip shape refinement for vowels is advantageous in that language learners can easily see the movement of articulators by eye, and it is helpful for learning and teaching English vowels for the hearing-impaired.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.591-600
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• 2016

Computing technologies are increasingly applied to most casual human environment networks, as computing technologies are further developed. In addition, the rapidly increasing interest in IoT has led to the wide acceptance of speech recognition as a means of HCI. In this study, we present a novel method for recognizing the Korean vowel 'ㅜ', as a part of a phoneme based Korean speech recognition system. The proposed method involves analyses of bulk indicators calculated in the time domain instead of analysis in the frequency domain, with consequent reduction in the computational cost. Four elementary algorithms for detecting typical waveform patterns of 'ㅜ' using bulk indicators are presented and combined to make final decisions. The experimental results show that the proposed method can achieve 90.1% recognition accuracy, and recognition speed of 0.68 msec per syllable.